Abstract
Given two pictures of the same scene captured using the same camera and the same lens, the one captured with a large aperture will appear partially blurred while the other captured with a small aperture will appear totally sharp. This paper investigates two possible ways of inferring depth of the scene from such an image pair with the constraint that both pictures are focused on the closest point of the scene. Our first method uses a series of Gaussian kernels to blur the image pair, and in the second method, the image pair will be shrunk to a series of smaller dimensions. In both methods, sharp areas in both images will always stay similar to each other, whereas the areas that appear sharp in one image but blurred in the other will not be similar until they are blurred using a large Gaussian kernel or shrunk to small dimensions. This observation enables us to roughly tell which objects in the scene are closer to us and which ones are farther away. At the end of this paper, we will discuss the limitations of our proposed approaches and some of the directions for future work.
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Li, Y., Au, O.C., Xu, L., Sun, W., Hu, W. (2013). Inferring Depth from a Pair of Images Captured Using Different Aperture Settings. In: Li, S., et al. Advances in Multimedia Modeling. Lecture Notes in Computer Science, vol 7733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35728-2_18
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DOI: https://doi.org/10.1007/978-3-642-35728-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35727-5
Online ISBN: 978-3-642-35728-2
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